A novel soft sensor model based on artificial neural network in the fermentation process

Some crucial process variables in fermentation process could not be measured directly. Soft sensor technology provided an effective way to solve the problem. There has been considerable interest in modeling a soft sensor by using artificial neural network (ANN) in bioprocess. To generate a more efficient soft sensor model, we proposed a novel soft sensor model based on artificial neural network (SS-ANN). By analyzing a grey-box model of fermentation process, the secondary variables were selected. In modeling, on-line measurable variables could be taken as the input of ANN and the output is the derivatives of immeasurable variables. The estimated values of immeasurable variables were calculated by integrating the outputs of the well-trained ANN. The novel SS-ANN is different from the general SS-ANN. Experimental results of erythromycin fermentation process showed the novel soft sensor model could estimate mycelia concentration, sugar concentration and chemical potency with higher accuracy and generalization ability than the general soft sensor based on ANN. The novel soft sensor modeling method provides the theory basis for selecting the secondary variables. The dynamic characteristic of the process is considered, the novel model improves the estimation accuracy and generation ability. It can be concluded that the soft sensor model mentioned in this paper is reasonable and effective.